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Page 16 of 28 Zhang et al. J Mater Inf 2024;4:34 https://dx.doi.org/10.20517/jmi.2024.64

welding contour extraction and BPNN training in the research.

Weld seam thickness prediction based on technologic parameters
In the context of predicting weld formation for tube-to-tube-sheet welding, the introduction of various
interfering factors diminishes the uniformity of the weld formation process, thereby complicating the
prediction of weld seam thickness [94,95] . Traditional predictive models primarily depend on welding
parameters, including welding current, V , welding period and δ which directly influence the welding
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outcome . The BPNNs are initially established in the research that relies exclusively on the welding
[96]
technical parameters to evaluate the predictive efficacy and identify potential avenues for enhancement. To
mitigate the adverse effects of incorrect labels and bolster the accuracy of BPNNs, the strategy for managing
outliers is implemented. The data points exhibiting clear errors, primarily due to misalignment of the
welding machine’s core axis, were excluded. As illustrated in Figure 13, although the predictions of weld
seam thickness show marked improvement following data screening, considerable deviations between
predicted and actual values persist. Consequently, the intricate interplay of multiple factors influencing weld
formation makes achieving an optimal weld appearance extremely challenging through parameter
adjustments alone. A deeper exploration of arc characteristics is essential to improve the predictive accuracy
of BPNNs.

Correlation analysis between arc length and welding voltage
The arc contains crucial information about the welding process and can be used to predict welding
[97]
performance . The arc shape directly reflects variations in welding process parameters and stability, closely
related to the size and stress conditions of the molten pool which significantly affect weld quality. Therefore,
leveraging arc geometry to predict welding performance is a practical approach. To evaluate the feasibility
of the method, the relationship between arc length and key welding parameters specifically welding voltage
is examined in the research. The 28th group is selected for the validation due to the slow V , which facilitates
s
the collection of a larger volume of image data over the welding trajectory. Additionally, the high pulse duty
cycle and prominent arc characteristics further enhance the data quality. The initial cycle of the welding
torch travel takes 43.6 s, during which 1,244 molten pool images were captured. Through the thresholding
technique, only the images recorded during the peak current phase are retained. Arc length variations over
time are then extracted in batches with Python. As shown in Figure 14, the fluctuations in arc length closely
align with the monitored voltage values, confirming the correlation between arc length and welding voltage.
The outcome demonstrates that the arc shape encapsulates valuable process information and can be applied
to predict welding performance. The subsequent step is to extract the arc's geometric features from the
welding images for further analysis.

Image processing for arc contour extraction
Efficient extraction of the arc contour requires specialized preprocessing techniques for the welding images,
including three-dimensional grayscale distributions, region of interest (ROI) extraction and image
enhancement. Firstly, the machine vision system processes images based on the distribution and gradient of
the grayscale values. Figure 15 presents the HDR images of pulsed TIG welding at both peak and base
currents, along with their three-dimensional grayscale distributions and the grayscale variation along the
direction of maximum arc length. The grayscale values approach saturation in the region illuminated by the
arc, while the values drop sharply outside the arc area indicating a steep gradient between the arc and
surrounding areas. The pronounced contrast allows for manageable extraction of the apparent arc contour
by applying an appropriate grayscale threshold.